1. Field of the Invention
The present invention relates to a dynamic range expanding apparatus of a video image, and more particularly to an expanding apparatus employing a histogram equalization with respect to a brightness distribution of a video image.
2. Description of the Related Art
A video image (e.g. a photograph) taken by an imaging system, such as a camera, contains a variety of different information about an object. In such an image, it frequently occurs that the brightness and the color information in the image is distorted or not distributed uniformly due to the quantity of light exposed into the object, the performance of the imaging system or the picture-taking conditions. In this case, a preprocessing step, such as a histogram equalization, improves the contrast of the image and thus makes it easy to analyze the image and to extract the characteristics from the video image in the later steps. In general, a device which performs such a preprocessing step to increase the contrast of the video image is called a "dynamic range expanding apparatus."
One such representative prior dynamic range expanding apparatus implementing AGC (automatic gain control) is disclosed in U.S. Pat. No. 4,719,350, "Radiation imaging enhancement". According to this dynamic range expanding apparatus, a low frequency or DC component is filtered from a video signal, and thus the dynamic range is reduced. Then, signal processing is performed where the bits per pixel are restricted.
This type of signal processing has the drawback in that minute contrast information is lost as the dynamic range is reduced. This system has a further disadvantage in that spatial adaptive processing is impossible since signal processing is performed in a unit of a frame.
Next, a dynamic range expanding apparatus using a homomorphic filter will be described in connection with FIG. 1.
FIG. 1 is a block diagram showing the homomorphic filter which forms the prior dynamic range expanding apparatus.
As shown in FIG. 1, the homomorphic filter is comprised of log transforming block 11, high pass filter 12 and exponent transforming block 13. The dynamic range expanding apparatus with such a homomorphic filter can be classified as an image enhancement using a transform operation. The details of the image enhancement is disclosed in Stockham, "Image Processing in the Context of a Visual Model", Proceedings of the IEEE, 60(7), Jul. 1972, pages 838-842, and Xie et al, "Towards the Unification of Three Visual Laws and Two Visual Models in Brightness Perception", IEEE Trans systems, Man Cybernetics, 19(2), March/April 1989, pages 379-382.
Referring to FIG. 1, the video image signal is transformed to a logarithm by a log transforming block 11, and thus the illumination and the reflectance components are extracted from the video image signal. The reflectance component is filtered by the high pass filter 12. The output of the high pass filter 12 is transformed to an exponent, which is the same as the inverse log, by the exponent transforming block 13. Moreover, the reflectance component is selectively amplified by the high pass filter 12.
A camera system based on the above dynamic range expanding apparatus is disclosed in U.S. Pat. No. 5,144,442 under the name of "Wide dynamic range camera". An input part of the camera system is illustrated in FIG. 2.
The input part, as is illustrated in FIG. 2, is comprised of a plurality of cameras 31, 32 and 33, a pixel selector 34 and an NTP (Neighborhood Transform Processing) block 35.
A plurality of video image signals are obtained from cameras 31,32 and 33, and each signal is selected respectively by the pixel selector 34. The output of the pixel selector 34 is provided with an internal module of the camera system, and inputted to the NTP block 35 simultaneously. In the NTP block 35, a neighborhood transform processing is performed with respect to each video image signal, and the signal obtained in such a way is provided with the internal module of the camera system along with the output of the pixel selector 34. The prior dynamic range expanding apparatus requires a field memory or other complicated circuitry since it must process the input with respect to a plurality of video image signals.
To improve the contrast of the video image more effectively, a histogram equalization technique performed on the brightness distribution of a video image is widely known. Such a technique is disclosed in Anil K. Jane, "Fundamental of Digital Image Processing", Prentice-Hall International Edition, pages 241-243.
The histogram equalization technique is a type of a image enhancement. When the brightness of the video image is concentrated on some region of an effective display area, the histogram equalization technique increases the contrast of the brightness range on that region, thereby causing the dynamic range to be expanded. However, the histogram equalization technique has a disadvantage that, when the contrast of the dense brightness range greatly increases, the contrast of the sparse brightness range decreases and a digital noise or quantization noise is likewise amplified.
To improve the above mentioned disadvantage, a modified histogram equalization technique has been proposed. However, the modified histogram equalization technique is realized through complicated circuitry and can not improve the contrast and the noise simultaneously.
A plurality of methods for applying the improvement of dynamic range of brightness level to a color image have been proposed. Most of these methods expand or reduce the dynamic range of the RGB signal that is a color signal of a video image. More specifically, one of these methods improves the color components individually. Another method is to extract a brightness signal from the video image, and then to control the extracted brightness signal uniformly in accordance with the expanding reducing information as to the dynamic range of the brightness signal. One method for uniformly controlling the RGB signal obtained from multiple video image inputs in accordance with a brightness information is disclosed in U.S. Pat. No. 5,247,366, "Color wide dynamic range camera". However, the prior art has a disadvantage that the detailed circuit is very complex since an individual multi-stage control of the color signal as well as a processing of multiple video image inputs is required. Next, a prior histogram equalization will be described in detail in connection with FIGS. 3, 4A-4D, and 5A-5C.
According to the prior histogram equalization, the brightness distribution of a video image is smoothed. Namely, when expanding the dynamic range of brightness levels in a dense image, the prior histogram equalization improves the contrast of the overall image.
When the brightness level is i (i={0, 1, . . . , L-1}, herein L is a brightness range) and brightness distribution function is h(i), a brightness transform function f(i), or "gray scale transform function," is defined as the following formula. ##EQU1##
The function P(i) in the above formula is defined as follows: ##EQU2##
When the brightness signal of an input video image is represented as y(x), herein x is a two dimensional vector notifying coordinates of a video image, and a transformed brightness signal y'(x), the two signals can be represented in the following formula: EQU y'(x)=f(y(x))
FIG. 3 is a block diagram showing the dynamic range expanding apparatus according to the prior histogram equalization.
As shown in FIG. 3, the dynamic range expanding apparatus includes a histogram generator 21, an integrator 22 and a memory 23.
The histogram generator 21 receives a brightness signal y of a video image, and determines a brightness distribution function h(i) with respect to the brightness signal (y). The brightness distribution function h(i) of the histogram generator 21 is output to the integrator 22. In the integrator 22, an integration of the brightness distribution function is performed, and the result of the integration is output to the memory 23 as a brightness transform function f(i). The memory 23 stores a plurality of LUTs (Look-Up Tables) as to a brightness transform function in advance. Thus, the memory 23 determines an LUT in accordance with the output of the integrator 22, and then outputs a transformed brightness signal y' C through the determined LUT in response to the brightness signal of an input video image y.
FIG. 4A is a graph showing a brightness distribution function h(i) as to a video image produced in very low light. FIG. 4B is a graph showing a brightness transform function f(i). In FIG. 4C, a distribution of a transformed brightness signal in FIG. 3 is illustrated, and in FIG. 4D, the result of low pass filtering of the distribution in FIG. 4C with respect to i is illustrated.
However, if the video image is modified by the above equalization method, the resulting contrast of the video image is lost in the range with low occurrence of the video image even though it might be improved over most of the range. Furthermore, when the video image is concentrated in a particular brightness level range, the quantization error or the digital noise is also increased by the brightness transform of the range.
To solve the above problem, a modified equalization method that improves the contrast appropriately has been proposed. According to the modified equalization method, the brightness transform function can be represented as the following formula. ##EQU3##
Herein, the variable k regulates the sensitivity of the brightness transform function.
FIG. 5A is a graph showing the brightness transform function f(i) when the variable k is set between 0 and 1. FIG. 5B is a graph showing a distribution of the transformed brightness h'i signal obtained through the transform function in FIG. 5A. FIG. 5C is a graph showing the result of low pass filtering of the distribution in FIG. 5B with respect to i.
Consequently, control of the variable k can regulate the dynamic range of the video signal. When this is properly implemented, the quantization error can be lessened without sacrificing the overall dynamic range. However, this method produces a trade-off between the dynamic range of the video signal and the quantization error. For example, when the dynamic range increases, the quantization error increases; and when the quantization error decreases, the dynamic range decreases.
According to the prior histogram equalization, the brightness transform function is determined by the integral operation of the brightness distribution. In this case, when the occurrence of the video image is concentrated in a particular range of a brightness level, the slope of the brightness transform function increases in that region. Using prior histogram equalization, however, the more the dynamic range of the video image is expanded, the more the quantization noise also increases.
Accordingly, a need remains for a dynamic range expanding apparatus that is capable of expanding the dynamic range of the video image by the brightness transform while decreasing the quantization noise.